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Biologic Pathway Analysis

生物途径分析

基本信息

批准号：
8552959
负责人：
Kenneth Buetow
金额：
$ 3.45万
依托单位：
DIVISION OF BASIC SCIENCES - NCI
依托单位国家：
美国
项目类别：
财政年份：
资助国家：
美国
起止时间：
至
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/8552959
关键词：
Actins American Behavior Benign Biological Assay Biological Process Biomedical Research Cancer Patient Clinical Collection Complex Cytoskeleton Data Databases Disease European FGFR2 gene Fibroblast Growth Factor Focal Adhesions Genes Genetic Genetic Transcription Genome Genotype Imagery Individual Informatics Introns Investigation Laboratories Length Logic MAP Kinase Gene Malignant Neoplasms Malignant neoplasm of prostate Measures Metric Modeling Molecular Molecular Analysis Molecular Profiling Nurses&apos Health Study Participant Pathologist Pathology Pathway Analysis Pathway interactions Pharmaceutical Preparations Phenotype Predisposition Process Regulation Reporting Research Research Personnel Resources Rosa Sampling Signal Transduction Source Structure Testing Therapeutic Woman base cancer genomics cancer therapy cancer type case control design human disease insight malignant breast neoplasm molecular scale novel programs response tool

项目摘要

Recent biomedical research has made great progress in unveiling the complexity of human disease, while technological breakthroughs now allow much more detailed analysis of molecular behavior. As a result however, experimental results are frequently too complex for synthesis in the traditional model of tracing disease through individual genes. Instead, molecular pathways are gaining prominence as a new framework for analytic research. Pathways integrate information from across the entire genome while mirroring real biological processes. Disruption of the benign behavior of a pathway as a whole, not necessarily a single component of the pathway, could be the basis for disease. As yet, there exists no robust or straightforward means to transform the large-scale molecular expression data common to most genetic studies into meaningful data at the pathway level. To facilitate this promising mode of investigation, the PathOlogist has been developed as a resource capable of systematic and efficient pathway-centric analysis of molecular data. The PathOlogist is a new tool designed to automatically analyze large sets of genetic data within the context of molecular pathways. The tool aims to facilitate both a quantitative and qualitative analysis of pathway behavior in a format accessible to both laboratory researchers and informatics analysts. Foremost, the PathOlogist uses RNA expression data to calculate 2 descriptive metrics - activity and consistency - for each pathway in a set of more than 500 canonical pathways (source: Pathway Interaction Database http://pid.nci.nih.gov). Activity scores provide ameasure of how likely the interactions within the pathway are to occur while consistencyscores provide a measure of pathway logic by comparing the expected with de factooutcome of interactions. Pathway scores can be generated for any number of samples,and for any subset of the entire pathway collection. The program then allows a detailed exploration of the results through integrated visualization of pathway components, structure, and scores, hierarchical clustering of pathways and samples, and statistical analyses designed to identify associations between pathway scores and clinical features such as cancer type or patient survival. The PathOlogist provides a powerful means of identifying common molecular processes implicated in disease. By viewing molecular behavior at the pathway level, the metrics generated by the PathOlogist often provide further insight into disease pathology than could be gained from individual gene-based analyses. The tool is already being used for such diverse applications as predicting response to cancer treatment and identifying molecular signatures associated with cancer phenotype.In addition to PathOlogist, the Buetow lab uses the Pathways of Distinction Analysis(PoDA). We applied PoDA to 2287 genotypes obtained from the Cancer Genomic Markers of Susceptibility (CGEMS) breast cancer study. Briefly, thesamples comprised 1145 breast cancer cases and a comparable number (1142) of matched controls from the participants of the Nurses Health Study. All the participants were American women of European descent. The samples were genotyped using the Illumina 550K arrays, which assays over 550,000 SNPs across the genome.To provide a preliminary assessment of the validity of PoDA with observational data, we first examined a SNP set comprising the four SNPs in intron 2 of FGFR2 that were reported to show significant association with case status in (59) . As expected, we see a significant difference. Next, we applied PoDA systematically to the pathways represented in PID (28) using CGEMS data. A total of 69453 SNPs in the data could be associated with at least one of the pathways. These SNPs were observed to represent 4446 unique genes and the most significant SNP for each gene was retained for further analysis. The Wilcoxon p-values for cases and controls were computed for each pathway, and the multiple hypotheses were corrected using FDR adjustment (60,61) and significant pathways were reassessed by resampling to dummy pathways to adjust for pathway size. The most significantly associated pathway is Focal adhesion. Interestingly, this pathway is already being targeted by novel cancer therapeutic drugs (62-64). Four networks: FGF signaling, MAPK signaling, regulation of actin cytoskeleton, and prostate cancer contained FGFR2. All yielded significance p-values, however, only regulation of actin cytoskeleton was significant in comparison to randomly generated pathways of the same length. To assess whether the result is solely due to the presence of FGFR2, we eliminated the FGFR2 SNP from the regulation of actin cytoskeleton pathway and recomputed the p values; while the p-value for the Wilcoxon test rose, it remained highly significant, suggesting that the association of actin cytoskeleton regulation with breast cancer is not driven solely by differences in FGFR2.

最近的生物医学研究在揭示人类疾病的复杂性方面取得了巨大进展，而技术突破现在允许对分子行为进行更详细的分析。然而，其结果是，实验结果往往过于复杂的综合在传统的模型跟踪疾病通过个别基因。相反，分子途径作为分析研究的新框架越来越突出。通路整合来自整个基因组的信息，同时反映真实的生物过程。整个通路的良性行为的破坏，不一定是通路的单个组成部分，可能是疾病的基础。到目前为止，还没有强大的或直接的方法来转换大规模的分子表达数据，共同的大多数遗传研究在通路水平上的有意义的数据。为了促进这种有前途的调查模式，病理学家已被开发为能够系统和有效的分子数据的路径为中心的分析资源。 PathOlogist是一种新的工具，旨在自动分析分子通路背景下的大量遗传数据。该工具旨在以实验室研究人员和信息学分析人员均可访问的格式促进对通路行为的定量和定性分析。最重要的是，PathOlogist使用RNA表达数据来计算2个描述性指标-活性和一致性-对于一组500多个典型途径中的每个途径（来源：途径相互作用数据库http://pid.nci.nih.gov）。活动性评分提供了途径内相互作用发生的可能性的衡量标准，而一致性评分通过比较预期的相互作用与事实结果来提供途径逻辑的衡量标准。可以为任何数量的样品和整个途径集合的任何子集生成途径评分。然后，该程序允许通过路径组件，结构和评分的综合可视化，路径和样本的分层聚类以及旨在识别路径评分与临床特征（如癌症类型或患者生存率）之间关联的统计分析来详细探索结果。病理学家提供了一个强有力的手段，确定共同的分子过程中牵连的疾病。通过在通路水平上观察分子行为，病理学家生成的指标通常可以提供比基于个体基因的分析更深入的疾病病理学见解。该工具已经被用于预测对癌症治疗的反应和识别与癌症表型相关的分子特征等多种应用。除了PathOlogist，Buetow实验室还使用了区分分析途径（PoDA）。我们将PoDA应用于从癌症易感性基因组标记（CGEMS）乳腺癌研究中获得的2287种基因型。简而言之，这些样本包括1145例乳腺癌病例和相当数量（1142例）的护士健康研究参与者的匹配对照。所有参与者都是欧洲血统的美国妇女。使用Illumina 550 K阵列对样品进行基因分型，该阵列测定整个基因组中超过550，000个SNPs。为了用观察数据提供PoDA有效性的初步评估，我们首先检查了包括FGFR 2内含子2中的四个SNP的SNP集，其被报道为与病例状态显着相关（59）。正如预期的那样，我们看到了显著的差异。接下来，我们使用CGEMS数据将PoDA系统地应用于PID（28）中表示的途径。数据中总共有69453个SNP可能与至少一种途径相关。观察到这些SNP代表4446个独特的基因，并且保留每个基因的最显著的SNP用于进一步分析。计算每种通路病例和对照的Wilcoxon p值，使用FDR校正（60，61）校正多个假设，并通过恢复至虚拟通路以调整通路大小来重新评估重要通路。最显著相关的途径是局灶性粘连。有趣的是，这种途径已经被新型癌症治疗药物靶向（62-64）。四个网络：FGF信号传导、MAPK信号传导、肌动蛋白细胞骨架调节和前列腺癌包含FGFR 2。所有产生的显着性p值，然而，只有调节肌动蛋白细胞骨架是显着的随机产生的途径相比，相同的长度。为了评估该结果是否仅仅是由于FGFR 2的存在，我们从肌动蛋白细胞骨架途径的调节中排除了FGFR 2 SNP，并重新计算了p值;虽然Wilcoxon检验的p值上升，但仍然非常显著，表明肌动蛋白细胞骨架调节与乳腺癌的关联不仅仅是由FGFR 2的差异驱动的。